Introduction

Primary production (PP) determination based on data derived from remote sensors, requires regional and temporal estimations of phytoplankton photosynthetic parameters such as maximum light utilization coefficient (α*) and maximum photosynthetic rate ( Pm*) (^{Platt and Sathyendranath 1988}, ^{Platt et al. 1991}, ^{Sathyendranath et al. 1996}). It is also necessary to consider the spectral dependence of photosynthetically active radiation (PAR, 400-700 nm) in the water column and the spatial and vertical variability of the chlorophyll-specific absorption coefficient (aɸ* ) (^{Behrenfeld and Falkowski 1997}) to estimate PP for a particular region.

Spatial and temporal variabilitites in phytoplankton photophysiology are an important source of uncertainty in the models used to estimate PP (^{Sosik 1996}). However, not all parameters required by the models for PP estimation with remote sensing data are available for every pixel (^{Platt and Sathyendranath 1988}). One of the main issues with PP estimation based on remote sensing data is the extrapolation of local determinations of spatial and temporal photosynthetic parameters and bio-optical properties to large regions and long periods of time. This constitutes one of the most important tasks in biological oceanography (^{Longhurst et al. 1995}, ^{Sathyendranath et al. 1995}). One way to solve this issue is to divide the ocean into regions with similar oceanographic characteristics (^{Longhurst et al. 1995}, ^{Sathyendranath et al. 1995}), where photosynthetic parameters derived from the photosynthesis-irradiance (P-E) curves remain almost constant accross a region, over a season, or in time.

Satellite ocean color sensors have increased the use of synoptic images of phytoplankton biomass to perform global estimates of oceanic carbon fixation through phytoplankton photosynthesis (^{Platt and Sathyendranath 1988}, ^{Behrenfeld and Falkowski 1997}) . Thus, remote sensors are a useful tool for ocean monitoring at large scales, since they also allow for synoptic coverage and the possibility of obtaining nearly real-time data (^{Sathyendranath and Platt 1989}).

If chlorophyll concentrations and surface irradiance (E _PAR ) are determined from remote sensing data, average α* and Pm* data can be used to make regional-scale estimates of PP with semi-analytical models that take different variability time scales into account. For example, in the southern region of the California Current the average values for α* and maximum quantum yield of photosynthesis (ɸ _max ) are expected to be greater during spring due to the abundance of larger phytoplankton such as diatoms and dinoflagellates and to the high light absorption coefficient of phytoplankton. In contrast, during summer and autumn, average Pm* values will be much higher due to the dominance of small phytoplankton cells that grow at high irradiances. Assuming that average α* and Pm* values adequately represent the response of phytoplankton to environmental changes in the area, PP estimates based on semi-analytical models could be comparable to the in situ PP.

In the present study phytoplankton photophysiology is studied by determining average photosynthetic parameter values and bio-optical properties in the southern region of the California Current, off Baja California (Mexico). This region is considered a transition zone due to the influence of cooler, less -saline subarctic waters and warmer, saltier water of tropical and subtropical origin (^{Durazo and Baumgartner 2002}, ^{Durazo 2009}). The seasonal variability of parameters is studied and the possibility for these parameter to be extrapolated to a regional scale is evaluated. Together with remote sensing data, PP is estimated to represent the oceanographic conditions of this transitional region.

Materials and methods

Oceanographic cruises were conducted in the southern region of the California Current, off Baja California, during each season in 1999 (Fig. 1) . Water samples were collected by the cruises at the depths corresponding to 100%, 50%, 30%, 20%, 10%, and 1% E _PAR in hydrographic stations occupied near local midday. Also, vertical profiles of spectral irradiance (E _d [ λ], μW cm^-2 nm^-1) and PAR (400-700 nm, mol photons m^-2 s^-1) were measured using a PRR-600 radiometer (Biospherical Instruments) which was lowered into the water over the stern of the ship.

Figure 1 Location of the IMECOCAL (Spanish acronym for the Mexican Research of the California Current program) hydrographic lines and stations (filled circles). The northern region (lines 100-110) is subdivided into coastal (N-C, from shore to stations 000.45) and oceanic (N-O, starting at stations 000.50) zones, and the central region (líneas 113-133) is also subdivided into coastal and oceanic zones (C-C and C-O, respectively). 

For chlorophyll (Chla) determination 2 L of seawater were collected at each depth; water was then passed through Whatman GF/F filters, which were frozen in liquid nitrogen. In the laboratory, phytoplankton Chla was extracted with 90% acetone at 4 ºC and in complete darkness for 24 h according to the recommendations by ^{Venrick and Hayward (1984)}. Chlorophyll concentration (mg m^-3) was determined by the fluorometric method (^{Yentsch and Menzel 1963}, ^{Holm-Hansen et al. 1965}) using a Turner Designs A10 fluorometer calibrated with pure Chla (Sigma).

In addition, 2 L of water were collected at each depth to determine the light absorption coefficient of particles (a _p [λ]). Water was filtered through GF/F filter, which were immediately frozen in liquid nitrogen. Light absorption by the total amount of particles retained in the filters was measured using a Varian Cary 1E UV-Vis spectrophotometer, according to the method described by ^{Kishino et al. (1985)}, ^{Mitchell (1990)}, and ^{Mitchell et al. (2003)}. The same procedure was followed to determine the light absorption coefficient of detritus (a _d [λ]). The light absorption coefficient of phytoplankton (a _ɸ [λ]) was calculated as the difference between ɑ _p (λ) and a _d (λ).

Water samples were also used for identifying and quantifying phytoplankton by fixation with 2 mL of Lugol solution neutralized with acetic acid. A 50-mL aliquot was placed in settling chambers for 24 h and subsequently analyzed under an inverted microscope following the technique described by ^{Utermöhl (1958)}.

In total 35 experiments were conducted to estimate in situ PP: 9 in spring, 11 in summer, and 15 in autumn. Water was collected at each depth to fill three 250-mL polycarbonate bottles, and these samples were inoculated with 100 μL (5 μCi) of NaH¹⁴CO₃ according to the method described by ^{Steemann-Nielsen (1952)}. Two clear bottles and one dark bottle were placed in transparent acrylic tubes and incubated for 2 h at the original sampling depths, at about midday local time. Primary production (mg Cꞏm^-3ꞏh ^-1) was calculated ccording to ^{Parsons et al. (1984)} by subtracting the amount of assimilated carbon in the dark bottle.

During the 4 cruises, 54 experiments were conducted to estimate P-E curves. Water for the experiments was collected at depths corresponding to 50% E _PAR , and it was used to fill 27 plain transparent polystyrene bottles for phytoplankton cultures with 250 mL capacity (Nunclon). Bottles were each

spiked with 5 μCi of NaH¹⁴CO₃ (^{Steemann-Nielsen 1952}) and placed in a Morel type incubator for 2 h under a light gradient supplied by a 500-W tungsten-halogen lamp (^{Babin et al. 1994}). Light intensity at each position of the sample was determined with a QSL-100 irradiometer (Biospherical Instruments). The emission spectrum of the lamp is not spectrally neutral, as it is directly dependent on wave length, with a minimum emission in the blue and maximum in the red (^{Bouman et al. 2000}). Carbon fixation rates (P, mg C m^-3 h^-1) were calculated from radioactive counts by subtracting the zero-time values .

The α* (mg C [mg Chla]^-1 h^-1/ mol photons m^-2 s^-1) and Pm* (mg C [mg Chla]^-1 h^-1) parameters were estimated using the hyperbolic function described by ^{Jassby and Platt (1976)}: P* = Pm* Tanh[(α* E _s )/Pm*], where P* is the carbon assimilation rate normalized to Chla content in the sample (P* , mg C [mg Chla]^-1 h^-1) and E _s is the irradiance of the lamp measured from inside each bottle. The light saturation parameter (E _k , μmol photons m^-2 s^-1) is defined as the Pm*/α* relationship.

The α* values derived from the P-E curves and the average light-specific absorption coefficient of phytoplankton (a-ɸL*) from the 50% E _PAR samples, corrected for the lamp’s spectrum, were used to calculate ɸ _max (mol C/mol photons) from the following equation: ɸ _max = 0.023 α*/a-ɸL* (^{Schofield et al. 1991}, ^{Sosik 1996}). The phytoplankton-specific spectral absorption coefficient ( aɸ*(λ)) values for each depth were corrected for the downward irradiance (E _d [λ]) measured by the PRR-600, and they were normalized to Chla to obtain the average values of phytoplankton-specific absorption coefficients for each depth ( a-ɸ(z)*). Moreover, α* was estimated for each depth in the euphotic zone (α(z)*) using the following equation: α(z)*= 43.2 ɸ _max a-ɸ(z)* (^{Schofield et al. 1993}).

To determine the Pm* vertical profiles ( Pm(z)*), a constant value was used for the first half of the euphotic zone (Z_eu), from the surface to 10% E _PAR , and a linear variation of Pm(z)*was considered for the second half of Z_eu. Between 10% and 1% E _PAR , these Pm* values represented 25% of their value in surface waters (^{Prézelin et al. 1987}, ^{Valdez-Holguín et al. 1998}).

The study area was divided into northern (31º1.2 - 28º57.2 N, 116º46.6 -117º38.7 W; lines 100-110) and central (29º22.9 -24º55.1 N, 112º49.1 -118º10.9 W; lines 113-133) regions acording to the physical and biological properties (^{Lavaniegos et al. 2002}). Each region was subdivided into 2 zones taking the coastal (36 to 145 km off the coast) and oceanic (180 to 290 km off the coast) stations into account (Fig. 1). Average (α(z)*and Pm(z)*) values were calculated for each zone and region in order to estimate regional average PP values using the semi-analytical model described by ^{Platt and Sathyendranath (1988)}:

The vertical distribution of chla (Chl[z]) was obtained using the surface values estimated with remote sensing data and the Chla profile parameters from the model by ^{Platt et al. (1988)}, which were calculated for the area off Baja California by ^{Millán-Nuñez et al. (1997)} for each region and each season of the year. The vertical profiles for irradiance (E _z [PAR]) measured throughout the water column with the PRR-600 were fitted to a regression model. The regression coefficients, together with the E _PAR values measured by remote sensors, were used to estimate average irradiance vertical profiles for each region per season.

Results

Phytoplankton Biomass

In general, Chla concentrations near the surface (10 m) were higher at the coastal than at the oceanic stations (Fig. 2). In winter, Chla concentration in the coastal zone of line 110 (northern region) was 2.8 mg m^-3, while in other coastal zones of the northern and central regions concentrations were >1.0 mg m^-3 (Fig. 2a). In spring Chla was >2.0 mg m^-3 at the coastal stations in both regions, with values up to 8.0 mg m^-3 in lines 103-110 and 113-120 (Fig. 2b). Chlorophyll concentration in the central region was high in the summer, with values greater than 2.0 mg m^-3 at the coastal stations of lines 113 (up to 3.5 mg m^-3) and 127 (Fig. 2c). In autumn, the highest value was 7.5 mg m^-3 and was recorded at the coastal stations of line 130 (Fig. 2d). The lowest Chla value in summer and autumn was recorded in the oceanic zone of both regions.

Figure 2 Spatial variability of chlorophyll concentration at 10 m depth measured at the sampling stations during each cruise in winter (a), spring (b), summer (c), and autumn (d). 

During winter, integrated Chla (C_int) showed a maximum of 141 mg m^-2 at station 110.35 and high values at the coastal stations of lines 113 and 120. A value greater than 100 mg m^-2 was also observed for the oceanic region (Fig. 3a). In spring, the highest C_int values were recorded at the coastal stations of lines 107 and 120, and the lowest in the oceanic zone (Fig. 3b). In summer high values were observed (>100 mg m^-2) at the oceanic stations in the central and southern portions of the central region (Fig. 3c). On the other hand, in autumn, C_int values were up to 419 mg m^-2 at station 130.55, and lower in the rest of the area (Fig. 3d). The average C_int value for the whole study period was higher in the coastal zone of the northern and central regions (40 and 49 mg m^-2, respectively), with the highest averages observed in spring and autumn. In the oceanic zone C_int the average was 20 and 29 mg m^-2 in both regions, respectively (Table 1).

Figure 3 Spatial variability of chlorophyll integrated for the water column from cruise measurements taken in winter (a), spring (b), summer (c), and autumn (d). 

Table 1 Average seasonal variability by region and zone for euphotic zone depth (Z_eu, m), mixed layer depth (MLD, m), integrated chlorophyll for Z_eu (C_int, mg m^-2), maximum light utilization coefficient ( *, [mg C (mg Chla)^-1 h^-1]/[ mol fotones m^-2 s^-1]), maximum photosynthetic rate ( Pm*, mg C [mg Chla]^-1 h^-1), the photoacclimation parameter (E _k , mol fotones m^-2 s^-1), maximum quantum yield of photosynthesis (ɸ _max , mol C/mol fotones), and measured primary production (PP, mg C m^-2 h^-1). Modeled PP (PP_m, mg C m^-2 h^-1) was estimated for all station in the IMECOCAL grid. Values in parentheses represent the standard error (SD) of the mean; single values with no SD represent a single datum for the zone. Data not available, NA. 

Cruise	Zeu	MLD	Cint	α*	Pm*	E k	ɸ max	PP	PPm
North region/Coastal zone
Winter	48 (7.96)	58 (6.79)	13 (1.97)	0.012 (0.001)	1.77 (0.19)	154 (13.62)	0.023 (0.002)	NA	48 (8.11)
Spring	46 (4.04)	41 (9.71)	49 (14.10)	0.030 (0.009)	3.97 (2.10)	116 (30.23)	0.070 (0.023)	103 (45.08)	156 (64.70)
Summer	78 (9.50)	26 (3.40)	39 (12.42)	0.004 (0.002)	3.33 (1.03)	1011 (139.47)	0.011 (0.003)	57 (7.00)	69 (12.17)
Autumn	65	5	58	0.009	3.73	414	0.028	90	71 (15.10)
North region/Oceanic zone
Winter	33 (2.50)	66 (7.50)	10 (1.88)	0.011 (0.002)	2.23 (0.06)	216 (25.56)	0.022 (0.002)	NA	17 (1.73)
Spring	62	84	24	0.015	1.83	122	0.031	29	26 (3.27)
Summer	67 (1.50)	39 (1.50)	17 (2.94)	0.003 (0.000)	2.84 (0.03)	945 (8.40)	0.007 (0.000)	41 (0.00)	32 (4.53)
Autumn	94 (8.77)	40 (3.46)	29 (1.11)	0.009 (0.003)	3.26 (0.53)	386 (63.68)	0.029 (0.001)	63 (7.65)	46 (4.35)
Central region/Coastal zone
Winter	43 (3.64)	44 (11.91)	34 (6.44)	0.024 (0.010)	3.96 (0.99)	196 (28.51)	0.042 (0.014)	NA	135 (16.20)
Spring	33 (4.84)	43 (12.24)	59 (14.43)	0.031 (0.006)	4.33 (0.89)	144 (9.88)	0.068 (0.009)	105 (46.00)	158 (30.08)
Summer	74 (10.82)	22 (5.79)	45 (10.20)	0.008 (0.001)	6.46 (0.84)	819 (96.12)	0.021 (0.002)	140 (60.48)	174 (19.88)
Autumn	74 (6.33)	17 (3.10)	57 (7.75)	0.021 (0.007)	8.18 (2.30)	434 (38.06)	0.067 (0.022)	139 (26.81)	153 (12.59)
Central region/Oceanic zone
Winter	82 (6.33)	80 (3.00)	23 (2.06)	0.009 (0.001)	1.70 (0.10)	192 (16.85)	0.016 (0.002)	NA	30 (2.06)
Spring	65 (8.81)	71 (8.73)	26 (1.57)	0.015 (0.003)	2.07 (0.39)	143 (12.01)	0.049 (0.008)	44 (7.88)	64 (9.42)
Summer	82 (3.14)	38 (0.91)	24 (3.69)	0.003 (0.000)	3.05 (0.44)	910 (68.79)	0.007 (0.001)	50 (1.50)	50 (3.36)
Autumn	91 (5.61)	20 (5.74)	44 (9.51)	0.018 (0.005)	7.85 (2.10)	466 (55.62)	0.055 (0.025)	141 (34.42)	116 (12.90)

Primary production

During the study period the average values of integrated PP (PP_m) for the euphotic zone varied between 56 and 114 mg C m^-2 h^-1 in winter and spring, respectively. Compared with the estimates for the autumn cruise, PP_m in the coastal zone of the northern region was 2.2 times higher in spring. In contrast, the PP_m values for the oceanic zone were 1.4 times higher during autumn. Average PP_m for the coastal zone of the central region was similar among the different cruises. However, estimated PP_m for the oceanic zone was 1.8 times higher in autumn compared to the rest of the year (Table 1). In general, observed PP_m values were higher than 100 mg C m^-2 h^-1 at the coastal stations of the central region in winter (Fig. 4a). In spring, 2 highly productive zones (>150 mg C m^-2 h^-1) were detected at the coastal stations for both the northern and central regions (Fig. 4b). During the summer and autumn cruises, PP_m estimates were higher than 150 mg C m^-2 h^-1 and showed a larger spatial extension in both regions, although PP_m estimates were highest in the coastal zone of the central region and to the south of this region (Fig. 4 c, d).

Figure 4 Primary production, estimated with the model proposed by ^{Platt and Sathyendranath (1988)}, for winter (a), spring (b), summer (c), and autumn (d). Estimates are based on average vertical profiles of the maximum light utilization coefficient and maximum photosynthetic rate by zone and region, and on remote-sensing data. 

The overall average for PP_m was the same as the in situ PP average (94 mg C m^-2 h^-1). There was a high correlation (r = 0.85) between PP_m and the PP estimate based on in situ ¹⁴C experiments (Fig. 5a). When the photosynthetic parameters, PAR, and in situ Chla corresponding to the 50% E _PAR depth were introduced to the semi-analytical model proposed by ^{Platt and Sathyendranath (1988)}, the linear regression model showed high correlation (r = 0.95) between PP_m and in situ data for that depth (Fig. 5b). The PP_m values estimated with the model for coastal waters of the northern and central regions was around 1.2 to 1.5 times higher than in situ PP estimates for spring and summer, whereas similar PP_m and in situ PP estimates were recorded for autumn (Table 1). Furthermore, PP_m was 1.2 times higher than in situ PP during spring but it was 0.9 and 0.8 times lower than in situ PP in oceanic waters of the central region during summer and autumn.

Figure 5 (a) Relationship between integrated primary production for the euphotic zone estimated with the model and that estimated with in situ measurements using the ¹⁴C experiments. (b) Relationship between in situ and modeled primary production at the depth corresponding to 50% of surface irradiance (E _PAR ). The photosynthetic parameters of the photosynthesis-irradiance curves at 50% E _PAR were used in the model. 

Discussion

The C_int estimates for the coastal zone were higher during spring and autumn than during winter and summer but lower than the estimates for the 2000-2002 period reported by ^{Gaxiola-Castro et al. (2010)}. Even though oceanographic conditions in 1999 were characteristic of a La Niña event, the effects on the ecosystem were apparently less intense then than during the La Niña event in 2002. In accordance with ^{Gaxiola-Castro et al. (2010)}, the study area was divided into 3 zones depending on Chla concentrations: oligotrophic ( 0.25 mg m^-3), mesotrophic (>0.25 and 1.0 mg m^-3), and eutrophic (>1.0 mg m^-3). The spatial distribution of Chla depends on coastal upwelling processes, which occur with greater intensity during spring (^{Durazo et al. 2010}); the prevailing southward flow of the California Current; and the anomalously cold conditions due to the La Niña event. These processes increase nutrient supply to the euphotic zone near the coast and enhance phytoplankton growth (^{Gaxiola-Castro et al. 2010}).

The variability in α* was apparently a response to the spatial and temporal changes in phytoplankton populations, which are primarily dominated by diatoms and dinoflagellates during spring. Furthermore, the changes seem to be related to phytoplankton photoacclimation and variations in a _ɸ (λ). In spring, phytoplankton was photoacclimated to low irradiances, with high a _ɸ (λ) values, which resulted in higher α* values in the coastal zone of the northern and central regions (Table 1). These differences in magnitude due to phytoplankton size have been previously reported for the same study area by ^{Gonzalez-Morales et al. (1993)}.

In general, average α* values were slightly higher than and Pm* values were similar to those reported by ^{Aguirre-Hernández et al. (2004)}. Both photosynthetic parameters were lower than those estimated by ^{Valdez-Holguin et al. (1998)} for a station off Ensenada but were similar to those estimated by ^{Gonzalez-Morales et al. (1993)}, and by ^{Schofield et al. (1993)}, who also reported seasonal variability in these photosynthetic parameters. ^{Espinosa-Carreón et al. (2015)} reported variability in photosynthetic parameters during 2005, with high α* values in winter and spring and high Pm* values in summer and autumn, similar to the behavior of data shown here. ^{Geider (1993)} reported minor dependence of α* on irradiance growth, with higher interspecific variability in diatoms (~5-fold). In cyanophytes, however, α* decreases as irradiance increases due to the decreasing phycobiliproteins: Chla ratio during adaptation to high irradiances (^{Foy and Gibson 1982}, ^{Kana and Gilbert 1987}).

The low Pm* estimates for winter and spring were apparently related to decreasing water temperature (13.0 to 17.0 ºC), which decreases phytoplankton metabolism. In summer and autumn the increase in the water temperature, higher growth irradiance, and presence of smaller phytoplankton cells (flagellates) in the water column could have contributed to the increase in Pm* due to photophysiological acclimatization to high irradiances. ^{Geider (1993)} observed changes in Pm* between different sizes of phytoplankton cells and observed that diatoms showed low Pm* at the same growth irradiance. Furthermore, ^{Valiela (1995)} demonstrated variability in Pm*, which increases with rising water temperature (see his figure 2-33).

The average ɸ _max values estimated for the present study were similar to those reported by ^{Schofield et al. (1993)} for the area off southern California. These authors found that the observed high spatiotemporal variability was due to changes in phytoplankton photophysiology and community composition. Moreover, variability in ɸ _max has been linked to changes in nutrient availability (^{Babin et al. 1996}, ^{Sathyendranath et al. 1996}, ^{Sosik 1996}), spectral irradiance, photoperiods and light field (^{Dubinsky et al. 1984}), temperature (^{Chamberlin and Marra 1992}), increasing cellular concentration of photo-protective carotenoids at high irradiances (^{Bidigare et al. 1989}), and the ratio of the absorption coefficient between blue and red light (^{Sathyendranath et al. 1996}).

The high ɸ _max estimates for the spring cruise were associated with the presence of larger phytoplankton cells, such as diatoms and dinoflagellates, and with high a _ɸ (λ) values. In contrast, low f _max estimates for summer and autumn were associated with the abundance of small phytoplankton cells. ^{Dubinsky et al. (1986)} observed a slight decrease in this parameter with high growth irradiance (>500 mol photons m^-2 s^-1). ^{Geider (1993)} concluded that ɸ _max is mainly independent of the irradiance in cells growing under high nutrient concentrations.

In spring 1999 coastal upwelling was more intense than usual off Baja California (^{Durazo and Baumgartner 2002}). High PP values associated with high Chla concentrations and higher diatom and dinoflagellate abundances were estimated for this season. In summer PP was high in both the northern and central regions, whereas in autumn values were higher than 150 mg C m^-2 h^-1 for both the coastal and the oceanic zones of the central region (Fig. 4, Table 1). ^{Durazo et al. (2010)} detected coastal upwelling in the northern region throughout the year, with higher intensity during spring, while coastal upwelling in the south occurred primarily during spring and summer. ^{Sosa-Ávalos et al. (2005)} reported strong northeasterly Santa Ana winds, which can fertilize coastal surface waters by coastal upwelling and vertical mixing (^{Castro et al. 2003}, ^{Trasviña et al. 2003}). This eolic contribution can increase PP off Baja California (Fig. 4d, Table 1).

Average PP values were similar to those reported by ^{Smith and Eppley (1982)} for the coastal zone off southern California, which were associated with coastal upwelling events. ^{Aguirre-Hernández et al. (2004)} and ^{Espinosa-Carreón et al. (2015)} reported lower integrated PP values for the region off Baja California in comparison to the values reported in this study, in which PP showed substantial seasonal variability.

The difference between PP_m and in situ PP estimates for coastal waters in the northern and central regions during spring was due to the increased abundance in larger phytoplankton cells, which can contribute to variability in photosynthetic parameters. Additionally, the high a _ɸ (λ) values, observed mainly in the coastal zones, affected the calculations of α(z)* and ɸ _max (^{Geider 1993}). During summer and autumn, the PP_m estimate for the coastal zones of both regions was similar to the in situ PP estimate. However, the observed in situ values were 20% higher than the PP_m values for the oceanic zones during autumn.

The increase in Chla concentration in the coastal zone during spring was associated with coastal upwelling intensity. This zone was considered eutrophic due to the high Chla values, whereas the oceanic zone was considered oligotrophic. Photosynthetic parameters showed seasonal variability in the region off Baja California, with higher α* and ɸ _max estimates for coastal waters in spring and high Pm* in autumn. Moreover, the values of the photosynthetic parameters and bio-optical properties adequately represent the spatial variability in the oceanographic conditions that prevailed in the southern region of the California Current off Baja California. Thus, the proposed photosynthetic parameters and bio-optical properties for each region and season in this transition zone (a confluence zone of water masses with contrasting carachteristics) contribute to improve the development of regional algorithms. These algorithms, together with remote sensing data, will allow extrapolating PP estimations to regional or larger scales to better understand seasonal and interannual variability in PP associated with mesoscale and large-scale physical processes.

The results show that the average profiles for the photosynthetic parameters off Baja California change seasonally. This variability has important implications in PP estimations if constant values for these parameters are considered during an annual cycle. Therefore, the estimated PP values might not properly represent conditions in the ecosystem. A better option to estimate PP for the region off Baja California is to use remote sensing data and regional averages of the photosynthetic parameters for each season of the year. In general, the values for water-column integrated PP estimated with average ( α(z)*and Pm(z)*) values and the model by ^{Platt and Sathyendranath (1988)} were similar to in situ PP estimates, with a correlation of 0.85. These results suggest that the photosynthetic parameters proposed for the region can be applied to semi-analytical models to properly estimate PP. However, more information about these parameters under different oceanographic conditions is needed to solve for PP variability at interannual scales and to determine the phytoplankton carbon fixation rate in this transition zone.